params__ =
input.readMessage(
ParamsDefaultEntryHolder.defaultEntry.getParserForType(),
extensionRegistry);
internalGetMutableParams()
.getMutableMap()
.put(params__.getKey(), params__.getValue());
bitField0_ |= 0x00000008;
break;
} // case 42
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
}
break;
} // default:
} // switch (tag)
} // while (!done)
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.unwrapIOException();
} finally {
onChanged();
} // finally
return this;
}
private int bitField0_;
private java.lang.Object name_ = "";
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The name.
*/
public java.lang.String getName() {
java.lang.Object ref = name_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
name_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The bytes for name.
*/
public com.google.protobuf.ByteString getNameBytes() {
java.lang.Object ref = name_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
name_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @param value The name to set.
* @return This builder for chaining.
*/
public Builder setName(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
name_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return This builder for chaining.
*/
public Builder clearName() {
name_ = getDefaultInstance().getName();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @param value The bytes for name to set.
* @return This builder for chaining.
*/
public Builder setNameBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
name_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private com.google.cloud.automl.v1beta1.BatchPredictInputConfig inputConfig_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>
inputConfigBuilder_;
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the inputConfig field is set.
*/
public boolean hasInputConfig() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The inputConfig.
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfig getInputConfig() {
if (inputConfigBuilder_ == null) {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
} else {
return inputConfigBuilder_.getMessage();
}
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setInputConfig(com.google.cloud.automl.v1beta1.BatchPredictInputConfig value) {
if (inputConfigBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
inputConfig_ = value;
} else {
inputConfigBuilder_.setMessage(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setInputConfig(
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder builderForValue) {
if (inputConfigBuilder_ == null) {
inputConfig_ = builderForValue.build();
} else {
inputConfigBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder mergeInputConfig(com.google.cloud.automl.v1beta1.BatchPredictInputConfig value) {
if (inputConfigBuilder_ == null) {
if (((bitField0_ & 0x00000002) != 0)
&& inputConfig_ != null
&& inputConfig_
!= com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()) {
getInputConfigBuilder().mergeFrom(value);
} else {
inputConfig_ = value;
}
} else {
inputConfigBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder clearInputConfig() {
bitField0_ = (bitField0_ & ~0x00000002);
inputConfig_ = null;
if (inputConfigBuilder_ != null) {
inputConfigBuilder_.dispose();
inputConfigBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder getInputConfigBuilder() {
bitField0_ |= 0x00000002;
onChanged();
return getInputConfigFieldBuilder().getBuilder();
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder
getInputConfigOrBuilder() {
if (inputConfigBuilder_ != null) {
return inputConfigBuilder_.getMessageOrBuilder();
} else {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
}
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>
getInputConfigFieldBuilder() {
if (inputConfigBuilder_ == null) {
inputConfigBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>(
getInputConfig(), getParentForChildren(), isClean());
inputConfig_ = null;
}
return inputConfigBuilder_;
}
private com.google.cloud.automl.v1beta1.BatchPredictOutputConfig outputConfig_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>
outputConfigBuilder_;
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the outputConfig field is set.
*/
public boolean hasOutputConfig() {
return ((bitField0_ & 0x00000004) != 0);
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The outputConfig.
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig getOutputConfig() {
if (outputConfigBuilder_ == null) {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
} else {
return outputConfigBuilder_.getMessage();
}
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setOutputConfig(com.google.cloud.automl.v1beta1.BatchPredictOutputConfig value) {
if (outputConfigBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
outputConfig_ = value;
} else {
outputConfigBuilder_.setMessage(value);
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setOutputConfig(
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder builderForValue) {
if (outputConfigBuilder_ == null) {
outputConfig_ = builderForValue.build();
} else {
outputConfigBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder mergeOutputConfig(
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig value) {
if (outputConfigBuilder_ == null) {
if (((bitField0_ & 0x00000004) != 0)
&& outputConfig_ != null
&& outputConfig_
!= com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()) {
getOutputConfigBuilder().mergeFrom(value);
} else {
outputConfig_ = value;
}
} else {
outputConfigBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder clearOutputConfig() {
bitField0_ = (bitField0_ & ~0x00000004);
outputConfig_ = null;
if (outputConfigBuilder_ != null) {
outputConfigBuilder_.dispose();
outputConfigBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder
getOutputConfigBuilder() {
bitField0_ |= 0x00000004;
onChanged();
return getOutputConfigFieldBuilder().getBuilder();
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder
getOutputConfigOrBuilder() {
if (outputConfigBuilder_ != null) {
return outputConfigBuilder_.getMessageOrBuilder();
} else {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
}
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>
getOutputConfigFieldBuilder() {
if (outputConfigBuilder_ == null) {
outputConfigBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>(
getOutputConfig(), getParentForChildren(), isClean());
outputConfig_ = null;
}
return outputConfigBuilder_;
}
private com.google.protobuf.MapField params_;
private com.google.protobuf.MapField internalGetParams() {
if (params_ == null) {
return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
}
return params_;
}
private com.google.protobuf.MapField
internalGetMutableParams() {
if (params_ == null) {
params_ = com.google.protobuf.MapField.newMapField(ParamsDefaultEntryHolder.defaultEntry);
}
if (!params_.isMutable()) {
params_ = params_.copy();
}
bitField0_ |= 0x00000008;
onChanged();
return params_;
}
public int getParamsCount() {
return internalGetParams().getMap().size();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public boolean containsParams(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
return internalGetParams().getMap().containsKey(key);
}
/** Use {@link #getParamsMap()} instead. */
@java.lang.Override
@java.lang.Deprecated
public java.util.Map getParams() {
return getParamsMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.util.Map getParamsMap() {
return internalGetParams().getMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public /* nullable */ java.lang.String getParamsOrDefault(
java.lang.String key,
/* nullable */
java.lang.String defaultValue) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.lang.String getParamsOrThrow(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearParams() {
bitField0_ = (bitField0_ & ~0x00000008);
internalGetMutableParams().getMutableMap().clear();
return this;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder removeParams(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
internalGetMutableParams().getMutableMap().remove(key);
return this;
}
/** Use alternate mutation accessors instead. */
@java.lang.Deprecated
public java.util.Map getMutableParams() {
bitField0_ |= 0x00000008;
return internalGetMutableParams().getMutableMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder putParams(java.lang.String key, java.lang.String value) {
if (key == null) {
throw new NullPointerException("map key");
}
if (value == null) {
throw new NullPointerException("map value");
}
internalGetMutableParams().getMutableMap().put(key, value);
bitField0_ |= 0x00000008;
return this;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
* * For Text Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
* * For Image Classification:
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
* * For Image Object Detection:
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
* * For Video Classification :
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
* * For Tables:
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
* * For Video Object Tracking:
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder putAllParams(java.util.Map values) {
internalGetMutableParams().getMutableMap().putAll(values);
bitField0_ |= 0x00000008;
return this;
}
@java.lang.Override
public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.BatchPredictRequest)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.BatchPredictRequest)
private static final com.google.cloud.automl.v1beta1.BatchPredictRequest DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.BatchPredictRequest();
}
public static com.google.cloud.automl.v1beta1.BatchPredictRequest getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public BatchPredictRequest parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictRequest getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}